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ryen-stuff/SkyFilms-V1
SkyFilms-V1 is a image-to-video model from ryen-stuff. Use it for the image-to-video task on the model card, and read the license before you ship it in a product. It is set up for diffusers. The card lists the license as apache-2.0.
<p align="center" <img src="assets/logo2.png" alt="Skyreels Logo" width="60%" </p
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From the Hugging Face model README
This repo contains Diffusers style model weights for Skyreels A1 models. You can find the inference code on SkyReels-A1 repository.
Overview of SkyReels-A1 framework. Given an input video sequence and a reference portrait image, we extract facial expression-aware landmarks from the video, which serve as motion descriptors for transferring expressions onto the portrait. Utilizing a conditional video generation framework based on DiT, our approach directly integrates these facial expression-aware landmarks into the input latent space. In alignment with prior research, we employ a pose guidance mechanism constructed within a VAE architecture. This component encodes facial expression-aware landmarks as conditional input for the DiT framework, thereby enabling the model to capture essential low- dimensional visual attributes while preserving the semantic integrity of facial features.
Some generated results:
<video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/62e34a12c9bece303d146af8/licoAeSaF-K8x7DO7SGUG.mp4"></video>
<video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/62e34a12c9bece303d146af8/5q0p2jyw183fcJoeq0dvF.mp4"></video>
<video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/62e34a12c9bece303d146af8/1aZweOIszlriQLRwSqnGq.mp4"></video>
<video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/62e34a12c9bece303d146af8/5bfjDxGZJf-5WnGpFHppw.mp4"></video>
If you find SkyReels-A1 useful for your research, welcome to cite our work using the following BibTeX:
@article{qiu2025skyreels,
title={SkyReels-A1: Expressive Portrait Animation in Video Diffusion Transformers},
author={Qiu, Di and Fei, Zhengcong and Wang, Rui and Bai, Jialin and Yu, Changqian and Fan, Mingyuan and Chen, Guibin and Wen, Xiang},
journal={arXiv preprint arXiv:2502.10841},
year={2025}
}